Answer set programming (ASP) is a widely used declarative programming paradigm based on the stable-model semantics. Over the years, the formalism has been extended with new language elements, advanced evaluation techniques have been developed, and efficient and expressive systems have been realized. These advancements turned ASP into a rich modeling language which was successfully applied to various tasks in artificial intelligence and beyond, including combinatorial problems and knowledge-intense tasks.

 

Driven by the needs of applications, ongoing research in the field comprises, for instance, the integration with other paradigms (such as constraint programming), distributed computation, temporal and stream reasoning. These developments aim at the ultimate goal of equipping ASP with the features needed for realizing modern information systems.

 

This workshop focuses on recent trends in the area of ASP, including novel techniques for the evaluation of ASP programs, extensions of the formalism, integration with other formalisms, advancements of systems, and benchmark suites. Furthermore, discussions of applications of ASP and its extensions in AI and industry are welcome.

Vladimir Lifschitz, University of Texas at Austin, USA

How We Studied the Input Language of gringo

 

Vladimir Lifschitz is among the most influential scientists in the field of Artificial Intelligence (AI). He started out his work at Stanford with John McCarthy and made significant contributions in the area of Non-monotonic Reasoning (NMR). This can be seen a precursor to his later groundbreaking work connecting NMR with Logic Programming that led to the definition of the stable models semantics, and thus the initial foundations of Answer Set Programming (ASP). Interview with Vladimir Lifschitz in KI – Künstliche Intelligenz 32(2-3), 213-218. Available here.

Gerhard Brewka, University of Leipzig, Germany
Strong Inconsistency

Gerd Brewka pioneered the area of nonmonotonic reasoning in Germany. He started with tutorials on nonmonotonic reasoning at the German AI spring school, KIFS, in the eighties and is internationally regarded as the German figurehead of the area since early on. Moreover, his career allowed him to experience various research environments starting from GMD (now Fraunhofer) in Saint Augustin, to ICSI at Berkeley, California, over TU Vienna, finally settling down at Leipzig University. Interview with Gerhard Brewka in KI – Künstliche Intelligenz 32(2-3), 219-221. Available here.

The programme of the TAASP 2018 workshop is to be found here.

Welcome by Thomas Eiter.	Participants of TAASP 2018.	Keynote by Vladimir Lifschitz.
Participants of TAASP 2018.	Vladimir Lifschitz	Words by Thomas Eiter.

TAASP is a non-archival venue and there will be no published proceedings. However, informal proceedings will be provided and the papers will be posted informally on the workshop website. Therefore, it will be possible to submit to other conferences and journals both in parallel and subsequent to TAASP 2018.

Workshop Co-chairs

• Wolfgang Faber, Alpen-Adria-Universität Klagenfurt, Austria
• Gerhard Friedrich, Alpen-Adria-Universität Klagenfurt, Austria
• Torsten Schaub, Universität Potsdam, Germany
• Thomas Eiter, Technische Universität Wien, Austria
• Stefan Woltran, Technische Universität Wien, Austria

Organization Committee

• Michael Morak, Alpen-Adria-Universität Klagenfurt, Austria
• Philipp Wanko, Universität Potsdam, Germany
• Markus Hecher, Technische Universität Wien, Austria
• Peter Schüller, Technische Universität Wien, Austria

This workshop is supported by:

The FWF START grant Y698 for the project Decodyn: Treating Hard Problems with Decomposition and Dynamic Programming

The Vienna Center for Logic and Algorithms at TU Wien – VCLA and

The Wolfgang Pauli Institute – WPI